Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia

Table 1 Patient’s characteristics, mean ± SD/n (%)

Clinical features	Training set (n = 161)		P value	Test set (n = 53)		P value
	Bowel resection (n = 41)	No bowel resection (n = 120)		Bowel resection (n = 15)	No bowel resection (n = 38)
Age (years)	72.15 ± 12.73	68.13 ± 18.16	0.306	63.53 ± 12.49	61.87 ± 15.26	0.898
Gender (male)	33 (80.5)	105 (87.5)	0.396	12 (80.0)	29 (76.3)	0.990
BMI (kg/m2)	23.52 ± 3.69	22.68 ± 3.32	0.177	22.73 ± 3.44	21.68 ± 2.81	0.254
ASA (%)			0.575			0.642
II	15 (36.6)	46 (38.3)		3 (20.0)	12 (31.6)
III	19 (46.3)	61 (50.8)		9 (60.0)	21 (55.3)
IV	7 (17.1)	13 (10.8)		3 (20.0)	5 (13.2)
Comorbidity (%)
Diabetes	3 (7.32)	7 (5.83)	0.999	3 (20.0)	2 (5.26)	0.258
Cardiovascular disease	10 (24.4)	18 (15.0)	0.258	5 (33.3)	6 (15.8)	0.297
Cerebrovascular disease	6 (14.6)	15 (12.5)	0.935	5 (33.3)	5 (13.2)	0.193
Respiratory disease	2 (4.9)	13 (10.8)	0.411	2 (13.3)	3 (7.9)	0.929
Malignant disease	3 (7.3)	12 (10.0)	0.842	0 (0.0)	5 (13.2)	0.340
Incarcerated time (> 4 hours)	32 (78.0)	73 (60.8)	0.046	13 (86.7)	22 (57.9)	0.046
Bowel obstruction	31 (75.6)	65 (54.3)	0.026	11 (73.3)	17 (44.7)	0.116
Peritonitis	34 (82.9)	67 (55.8)	0.004	12 (80.0)	19 (50.0)	0.092
Laboratory parameters
CRP (mg/L)	38.39 ± 33.04	24.25 ± 35.43	0.001	28.03 ± 25.10	18.11 ± 17.20	0.126
WBC (× 109/L)	8.97 ± 3.85	8.58 ± 4.10	0.555	9.47 ± 3.60	8.21 ± 3.01	0.319
PLT (× 109/L)	217.83 ± 76.33	209.47 ± 94.10	0.501	223.13 ± 60.23	236.26 ± 138.80	0.953
ALB (g/L)	35.72 ± 9.54	38.46 ± 7.66	0.156	38.11 ± 7.05	39.39 ± 6.95	0.521
HB (g/L)	132.44 ± 26.10	138.81 ± 19.77	0.095	131.47 ± 24.98	132.00 ± 17.89	0.921
D-D (μg/L)	1003.41 ± 776.70	665.61 ± 611.18	0.001	1011.33 ± 483.38	842.11 ± 470.08	0.213
Surgical approaches
High ligation	18 (43.9)	8 (6.7)	0.000	5 (33.3)	5 (13.2)	0.091
Bassini	17 (41.5)	18 (15.0)	0.001	6 (40.0)	7 (18.4)	0.100
Lichtenstein	6 (14.6)	90 (75.0)	0.000	4 (26.7)	24 (63.2)	0.017
TAPP	0 (0.0)	4 (3.3)	0.236	0 (0.0)	2 (5.3)	0.365
Patch repair	6 (14.6)	94 (78.3)	0.000	4 (26.7)	26 (68.4)	0.006
Complications
Incision infection	6 (14.6)	6 (5.0)	0.043	3 (20)	2 (5.3)	0.098
Effusion or hematoma	2 (4.9)	14 (11.7)	0.210	2 (13.3)	3 (7.9)	0.542
Atelectasis	1 (2.4)	4 (3.3)	0.776	0 (0.0)	2 (5.3)	0.365
Uroschesis	3 (7.3)	5 (4.2)	0.423	2 (13.3)	1 (2.6)	0.129
Abdominal abscess	3 (7.3)	0 (0.0)	0.015	1 (6.7)	0 (0.0)	0.108
Duration of hospitalization (day)	8.29 ± 3.66	7.43 ± 3.18	0.152	9.93 ± 5.37	7.84 ± 3.28	0.090

BMI: Body mass index; ASA: American Society of Anesthesiologists; HB: Hemoglobin; WBC: White blood cell; PLT: Platelet; ALB: Albumin; CRP: C-reactive protein; D-D: D-dimer; TAPP: Transabdominal preperitoneal patch plasty.

Table 2 Multivariable logistic regression analysis of clinical features between patients with bowel resection and without bowel resection in the training set

Clinical features	P value	OR	95%CI
Incarcerated time, > 4 hours	0.060	2.406	0.965-6.000
Bowel obstruction (%)	0.012	3.153	1.291-7.700
Peritonitis (%)	0.003	4.207	1.608-11.011
CRP (mg/L)	0.019	1.013	1.002-1.024
D-D	0.042	1.001	1.000-1.001

CRP: C-reactive protein; D-D: D-dimer; OR: Odds ratio; CI: Confidence interval.

Table 3 Predictive performance of radiomic signature in multiple models

Model	Set	AUC (95%CI)	ACC	SEN	SPE	PPV	NPV
LR	Training	0.828 (0.7523-0.902)	0.832	0.610	0.908	0.694	0.872
	Test	0.791 (0.668-0.915)	0.698	0.933	0.622	0.483	0.958
SVM	Training	0.919 (0.859-0.979)	0.863	0.927	0.842	0.667	0.971
	Test	0.742 (0.599-0.885)	0.717	0.800	0.703	0.500	0.897
KNN	Training	0.840 (0.780-0.901)	0.783	0.756	0.792	0.554	0.905
	Test	0.613 (0.463-0.764)	0.528	0.867	0.417	0.361	0.882
RF	Training	1.000 (0.999-1.000)	0.988	1.000	0.983	0.953	1.000
	Test	0.603 (0.434-0.771)	0.717	0.267	0.944	0.500	0.756
ET	Training	1.000 (1.000-1.000)	1.000	1.000	1.000	1.000	1.000
	Test	0.583 (0.418-0.748)	0.604	0.600	0.622	0.375	0.793
XGBoost	Training	1.000 (1.000-1.000)	1.000	1.000	1.000	1.000	1.000
	Test	0.665 (0.517-0.813)	0.623	0.867	0.526	0.419	0.909
LightGBM	Training	0.963 (0.936-0.989)	0.907	0.927	0.900	0.760	0.973
	Test	0.627 (0.479-0.776)	0.528	1.000	0.351	0.375	1.000
MLP	Training	0.829 (0.760-0.899)	0.770	0.780	0.767	0.533	0.911
	Test	0.686 (0.536-0.836)	0.604	0.867	0.514	0.406	0.905

LR: Logistic regression; SVM: Support vector machine; KNN: K-nearest neighbor; RF: Random forest; XGBoost: EXtreme gradient boosting; LightGBM: Light gradient boosting machine; ET: Extra trees; MLP: Multilayer perceptron; AUC: Area under the curve; CI: Confidence interval; ACC: Accuracy; SEN: Sensitivity; SPE: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.

Table 4 Predictive performance of clinical model, radiomic signature and nomogram in the training and test sets

Group	Signature	AUC (95%CI)	ACC	SEN	SPE	PPV	NPV
Training set	Clinic signature	0.760 (0.679-0.840)	0.714	0.707	0.723	0.460	0.878
	Rad signature	0.828 (0.753-0.902)	0.832	0.610	0.908	0.694	0.872
	Nomogram	0.864 (0.800-0.929)	0.845	0.707	0.892	0.690	0.899
Test set	Clinic signature	0.747 (0.598-0.897)	0.811	0.533	0.946	0.727	0.833
	Rad signature	0.791 (0.668-0.915)	0.698	0.933	0.622	0.483	0.958
	Nomogram	0.800 (0.669-0.931)	0.830	0.667	0.895	0.714	0.872

AUC: Area under the curve; CI: Confidence interval; ACC: Accuracy; SEN: Sensitivity; SPE: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.